Most AI prototyping lists mix two different kinds of tool into one ranking. This guide splits nine of them into design-first and code-first, shows what each one is genuinely for, and says which outputs ship and which get rebuilt.
Design-first or code-first? Pick the camp before you pick the tool.
There are two kinds of AI prototyping tool, and picking from the wrong half is the most expensive mistake teams make with them.
One kind gives you something to look at. You describe a product, it produces screens, and you use those screens to test whether the flow makes sense before anyone writes code. The other kind gives you something that runs. You describe a product, it produces working code, and what comes back either becomes your first version or gets thrown away.
Both are useful. They solve different problems. And most tool roundups mix them into one list ranked by "best," which is how a founder ends up spending three weeks in a code-generation tool when all they needed was a clickable mockup for five user interviews.
This guide sorts nine tools into those two camps, tells you what each one is genuinely for, and ends with a decision framework based on the only question that matters: what happens to the prototype after the demo. It's written for founders, product leads and designers at early-stage companies. We're a design agency, we use several of these on client work, and we'll be specific about where they stop being enough.
TL;DR
Design-first tools generate screens to test look and flow. Code-first tools generate working software to test behaviour. Pick the camp before you pick the tool.
If the prototype is disposable, use a design-first tool and move fast. If it might become version one, start code-first.
Figma Make is the default if your team already lives in Figma. Lovable is the default if nobody on the team codes.
The tools compress the build. They don't decide what to build, and that's still the part that decides whether the product works.
Tools at a glance
Tool | Type | Best for | Pricing model | Ships or rebuilds |
Figma Make | Design-first | Teams already in Figma | Included in paid Figma plans | Rebuilds |
Uizard | Design-first | Fast screens from a prompt | Free tier plus paid | Rebuilds |
Relume | Design-first | Sitemaps and structure first | Free tier plus paid | Rebuilds, or exports to Webflow |
Banani | Design-first | Throwaway concepts | Free tier plus paid | Rebuilds |
v0 by Vercel | Code-first | React and Tailwind teams | Credit-based | Ships, with work |
Lovable | Code-first | Non-technical founders | Credit-based | Ships, with work |
Bolt.new | Code-first | Full-stack in the browser | Credit-based | Ships, with work |
Replit | Code-first | Prototypes meant to become products | Subscription plus usage | Ships |
UXPin | Code-first | Design backed by real components | Per-seat subscription | Ships |
Pricing in this category changes constantly, so check current figures before you commit budget. The column above describes how each tool charges rather than what it charges today.
How we picked these nine
Four things, applied to every tool on the list.
It produces something you can click. Plenty of AI design tools generate static images. Those are useful and they belong in a different roundup, which is our wider list of AI UX design tools. Everything here outputs an artefact somebody can move through.
It's usable by a small team. No enterprise-only tooling, no six-week onboarding. If a three-person startup can't be productive in it inside a day, it's the wrong recommendation for this audience.
It has a clear centre of gravity. Tools that claim to do everything tend to do nothing in particular. Each of these is noticeably better at one job than at the others, and that's what makes the list useful rather than nine descriptions of the same thing.
It's still actively developed. This category produces abandonware at an impressive rate. Anything here has shipped meaningful updates recently.
A few well-known tools didn't make it. Some are excellent at generating individual screens but have no way to link them into a flow. Others are genuinely good and overlap almost entirely with a tool already on the list, in which case we picked one rather than padding the count.
What AI prototyping software actually is
An AI prototyping tool takes a description, a sketch, or an existing design and generates an interactive artefact you can click through. Instead of building every screen by hand, you describe what you want and refine what comes back.
That's the whole category. It sits inside a much larger stack, and if you're assembling one from scratch our guide to UX design tools covers the fundamentals that prototyping software assumes you already have. What separates the tools on this list is what the artefact is made of.
A UX prototype has always been a stand-in for the real product, built to answer a question before you commit engineering time. AI hasn't changed the purpose. It's changed how long it takes to get one in front of someone, which turns prototyping from a milestone into something you can do on a Tuesday afternoon. That shift is part of a wider change in how AI is transforming UX practice, and prototyping is where it has landed hardest.
Design-first vs code-first
This is the distinction worth internalising.
Design-first tools generate visual interfaces. You get screens, states and clickable links between them. What you're testing is whether people understand the layout, find what they need, and follow the flow you intended. The output is a picture that behaves like a product.
Code-first tools generate working software. You get components, logic and sometimes a back end. What you're testing is whether the thing actually works, whether the interaction feels right at real speed, and whether the technical approach holds. The output is a product that happens to be rough.
The trap is assuming code-first is simply the more advanced option. It isn't. If your question is "does this onboarding flow make sense," a code-first tool gives you a slower, more expensive way to answer it, and you'll spend your time debugging instead of learning. If your question is "can we actually pull this off," a design-first mockup will tell you nothing.
Pick by question, not by capability.
What changed in 2026
Two shifts are worth knowing before you evaluate anything, because they're recent enough that older comparisons miss them.
Generation became iterative. The first wave of these tools did one shot: prompt in, output out, and if it was wrong you started again. Most of the code-first tools now run as agents instead. They plan, generate, run what they made, notice the error, and fix it before handing anything back. Practically, that means the useful unit of work has moved from "write a perfect prompt" to "have a conversation," and teams who still treat these tools as one-shot generators get noticeably worse results than teams who don't.
The prototype-to-product line got blurry, and that's a mixed blessing. When generated code was obviously throwaway, nobody argued about whether to keep it. Now that output is plausible enough to ship, teams keep things they'd have rebuilt a year ago. Sometimes that's the right call and it saves weeks. Sometimes you inherit an architecture nobody chose, and the cost surfaces three months later when the second engineer joins. Decide deliberately which one you're doing rather than letting the tool's confidence decide for you.
The practical version: judge a tool on how well it recovers from being wrong, not on how good its first output looks. First outputs are uniformly impressive now. The difference between tools shows up on the fourth iteration.
That shift has changed daily practice more than most people expected, and not only in prototyping. We've tracked the wider version of it in our roundup of AI tools for UI UX designers.
Where prototypes sit next to wireframes
Worth being precise, because AI tools have blurred the boundary.
Low-fidelity wireframes are about structure: what goes on the page, in what order, at what priority. They're deliberately ugly so nobody argues about colour. High-fidelity wireframes add real content and visual detail but still don't move. A prototype is the one you can click.
Most AI tools skip straight to high fidelity, which is a real change in the workflow and not always a good one. A polished screen invites feedback on the polish. A rough one invites feedback on the idea. If you want structural feedback, you may have to deliberately ask for something uglier than the tool wants to give you. Our breakdown of wireframes vs prototypes covers where each belongs in a project.
The 10 best AI prototyping tools for 2026
Design-first
1. Figma Make
Best for: teams whose design work already lives in Figma.
Figma Make generates and iterates on prototypes inside Figma itself, using the file, components and styles you already have. That last part is the whole argument for it. Every other tool on this list starts from nothing and hands you something you then have to reconcile with your design system. Figma Make starts from your system.
Where it struggles: it inherits Figma's boundaries. If you want to test something structurally different from how your product works today, starting inside your existing file can anchor you to the current thing. And if your team doesn't already work in Figma, the main advantage evaporates, which makes the underlying platform choice the real decision. Our comparison of Figma, Sketch, or Adobe XD is the place to settle that one.
Ships or rebuilds: rebuilds. Output is design, not production code, though the Figma to code path is shorter than it used to be.
2. Uizard
Best for: getting usable screens out of a text prompt in minutes.
Uizard, now part of Miro, generates multi-screen concepts with assets and working links from a description or a rough sketch. It's the fastest route from "I have an idea" to "I have something to show the team," and it handles the unglamorous work of producing several consistent screens rather than one hero shot.
Where it struggles: speed comes at the cost of distinctiveness. Output tends toward familiar patterns, which is fine for testing a flow and limiting if you're trying to explore a novel interaction.
Ships or rebuilds: rebuilds. Treat the output as a communication artefact.
Worth knowing: Google Stitch covers similar ground with a free tier, and is worth testing alongside if budget is the constraint.
3. Relume
Best for: getting the structure right before anything gets designed.
Relume works backwards from most of this list. It generates a sitemap first, then wireframes for each page, then visual design. For a marketing site, where the real question is usually what pages exist and what each one has to say, that order is correct and almost nobody else follows it.
Where it struggles: it's built for sites, not products. Ask it for an application with real state and it's the wrong tool. The component library is also opinionated, so output has a recognisable house style. If the whole project is a website rather than a product, you're better served by the site-specific options in our AI web design tools roundup.
Ships or rebuilds: exports to Webflow, so the structure survives even if the visual design gets replaced. If you're choosing a platform for the finished site, Framer vs Webflow works through the trade-off.
4. Banani
Best for: concepts you intend to throw away.
Banani is built for speed over permanence. Describe a screen, get a screen, decide whether the idea has legs, discard it if not. That sounds dismissive and isn't. Most product ideas should be discarded, and the cheaper it is to reach that verdict the more ideas you can put through the process.
Where it struggles: fidelity and control. If you need a specific visual direction or a design-system-accurate screen, you'll fight it.
Ships or rebuilds: rebuilds, by design.
Galileo AI occupies similar territory and is worth a look if Banani's output doesn't suit your aesthetic.
Code-first
5. v0 by Vercel
Best for: product teams who already work in React.
v0 generates interface code in React with Tailwind, the stack a large share of product teams already use. What comes back is a component you can paste into a real codebase rather than a mockup someone has to rebuild, which changes the economics of prototyping entirely when the stack matches.
Where it struggles: the stack assumption. If you're not in React, the advantage inverts and you're translating output rather than using it. Complex application state also still needs a human.
Ships or rebuilds: ships, with cleanup. Output is real code, not production-quality code.
6. Lovable
Best for: founders who can't code and need something that works.
Lovable turns a description into a running application, including a back end, without the user touching a terminal. For a non-technical founder trying to validate an idea before recruiting a technical co-founder or spending on development, this is the most direct path that exists right now.
Where it struggles: the gap between "it works in the demo" and "it works for a hundred users" is large and doesn't announce itself. It's easy to mistake a functioning prototype for a functioning product.
Ships or rebuilds: ships as a starting point. Plenty of teams have taken a Lovable build into production, and plenty have rebuilt it once the requirements got real.
If validation is what you're after, our guide to MVP UX design covers how much product you actually need before you learn something.
7. Bolt.new
Best for: full-stack prototypes built by people comfortable near code.
Bolt runs a development environment in the browser and generates front end and back end together. It's quick and it assumes you can read what it produces and intervene. For a technical founder or a developer exploring an approach, that's a good trade.
Where it struggles: it's less forgiving than Lovable for non-technical users. When generation goes wrong, you need to understand why.
Ships or rebuilds: ships, with work.
8. Replit
Best for: prototypes you expect to grow into the real product.
Replit's agent builds inside a full development environment with hosting, databases and deployment attached. The difference from the rest of the code-first group is continuity: you're not prototyping in one place and rebuilding somewhere else, you're starting the product in the place it will live.
Where it struggles: that continuity is a commitment. You're choosing an environment, not just a prototyping tool, and switching later costs more than throwing away a mockup.
Ships or rebuilds: ships. That's the point of it.
9. UXPin
Best for: teams who want design fidelity and real component behaviour in the same artefact.
UXPin prototypes can be backed by actual code components from your React library or Storybook, so what a stakeholder clicks behaves the way the built product will. That closes the gap where a prototype tests beautifully and then behaves differently once engineering gets hold of it.
Where it struggles: it needs a component library to point at. Without a real design system in code, most of the advantage disappears and you're using a more complex tool for a simpler job.
Ships or rebuilds: ships, in the sense that the components are already production components.
This is the tool that most rewards having your system in order first. If you're not there yet, AI design systems covers how teams are building and maintaining them now.
10. Jotform Apps
Best for: creating functional business apps through conversation inside ChatGPT.
Jotform’s ChatGPT App Builder turns natural language prompts into ready-to-use no-code apps. Users can describe what they need, then customize layouts, branding, widgets, and app components through conversation. They can add forms, interactive workflows, and business automation, manage users and permissions, and accept payments. Supported use cases include internal tools, customer portals, CRM apps, event registration apps, booking systems, and business dashboards.
Where it struggles: the workflow relies on conversational prompts, so users need to describe their requirements clearly and communicate the changes they want as they customize the app.
Ships or rebuilds: ships. Users can publish and share the functional no-code app after customizing it.
How to choose
Two questions get you most of the way.
By what happens after the demo
It gets thrown away. You're testing an idea, not an implementation. Banani or Uizard. Move fast, spend nothing, don't get attached.
It needs to actually run. You're testing behaviour, performance or feasibility. v0 if you're in React, Lovable if nobody codes, Bolt.new if someone does.
It becomes version one. Replit, or UXPin if you already have a component library worth building on.
By where you start
From a blank prompt: Uizard, Banani, Lovable.
From an existing design file: Figma Make.
From a sitemap or content plan: Relume.
From an existing codebase or component library: v0, UXPin.
Two other things to weigh. How comfortable is your team reading and fixing generated code, because every code-first tool assumes some of that. And how much does visual fidelity matter for the decision you're trying to make, because a rough prototype often produces better feedback than a polished one.
If the budget is zero, start with the free tiers rather than the paid comparison. Several of these are generous enough to answer a first question, and our list of free UI UX design tools covers what else you can assemble around them without spending anything.
From prototype to something real
The prototype is not the deliverable. What you learn from it is.
A workable sequence, whichever tool you pick:
Write the question down first. "Will people understand what this does" and "can we build this in six weeks" are different questions and they point at different tools.
Generate roughly, then edit hard. First output is a draft. The teams who get value here are the ones who treat generation as the starting point rather than the answer. If you want a sense of what good looks like at each fidelity level, these prototype examples are a useful calibration.
Put it in front of five people. Not colleagues. People who resemble your users. This is the step that gets skipped and it's the only one that produces new information.
Decide what survives. Some prototypes become the product. Most should contribute a decision and then be deleted.
If you're moving from structure into something interactive, our step-by-step on going from wireframe to prototype covers where the handoffs usually break.
What AI prototypes still can't do
Being honest about this is more useful than another feature list.
They can't tell you what to build. A tool will happily generate a beautiful interface for a product nobody wants. The research that decides what's worth building still happens before you open any of these.
They can't make judgement calls. Generated output is competent and generic, because it's assembled from patterns. The decisions that make a product feel considered, what to leave out, what to emphasise, where to break a convention on purpose, are still human. We've written about the specific ways this goes wrong in AI UX design mistakes.
They can't catch the gap between demo and reality. Prototypes run on clean data with one user and no edge cases. Products don't. Empty states, error states, slow connections, and the person who does the unexpected thing are all invisible until someone deliberately looks for them.
They can't do visual identity. Generated UI defaults to the aesthetic mean. If brand distinctiveness matters, that's separate work. Tools like Midjourney handle visual exploration, and neither category replaces an actual art direction decision.
None of this argues against using them. It argues for knowing which part of the job they're doing. Our wider view on working this way is in AI-driven design.
How this played out on a real project
When we designed PathwaysX, an AI-powered B2B hiring platform, the sequence looked like this. Low-fidelity wireframes to settle structure. A UI system so screens stayed consistent as the product grew. High-fidelity prototypes for the three flows that carried the most risk: job creation, candidate assessments and interview scheduling. Then testing, then development.
The prototypes did one specific job. They let us find out that the assessment flow made sense to a hiring manager before anyone built it. The tooling made that faster. It didn't decide which three flows were worth prototyping, and that decision came from research, not from a prompt.
The other thing worth reporting from that project: the prototype we learned most from was not the most polished one. The assessment flow went through a deliberately plain version first, because a hiring manager looking at a finished-looking screen tells you about the button colour, and one looking at a rough screen tells you the step order is wrong. That's a workflow decision the tools won't make for you, and it's the one that changes what you learn.
That's the shape of it on most projects. Tools compress the making. The thinking that decides what to make is where the outcome is determined, which is the argument we make at greater length in AI product design.
So which one should you use?
If you're in Figma and need something clickable this week, Figma Make. If nobody on your team codes and you need something that runs, Lovable. If you're a React team, v0. If the prototype is meant to become the product, Replit.
And if you're not sure which question you're answering yet, that's the thing to settle first. It takes an afternoon and it's worth more than any tool on this list.
We design products for SaaS and AI companies, and the part we're usually brought in for is what happens either side of the prototype: working out what's worth testing, and turning a generated draft into something with a point of view. If that's where you're stuck, book a discovery call and we'll take a look at what you have.
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